class_docstr: Ranked List Loss described in https://arxiv.org/abs/1903.03238
     Default parameters correspond to RLL-Simpler, preferred for exploratory analysis.
  
  Args:
      * margin (float): margin between positive and negative set
      * imbalance (float): tradeoff between positive and negative sets. As the name suggests this takes into account
                          the imbalance between positive and negative samples in the dataset
      * alpha (float): smallest distance between negative points
      * Tp & Tn (float): temperatures for, respectively, positive and negative pairs weighting.
code: def get_default_distance(self):
      return LpDistance()
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/ranked_list_loss.py
imports: import warnings
  import torch
  from ..distances import LpDistance
  from ..utils import common_functions
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 3 for calling the RankedListLoss obj's underlying member method code with example usage: obj.get_default_distance() and returns: LpDistance